Goals and the Structure of Experience

Purposeful behavior is a hallmark of natural and artificial intelligence. Its acquisition is often believed to rely on world models, comprising both descriptive (what is) and prescriptive (what is desirable) aspects that identify and evaluate state of affairs in the world, respectively. Canonical computational accounts of purposeful behavior, such as reinforcement learning, posit distinct components of a world model comprising a state representation (descriptive aspect) and a reward function (prescriptive aspect). However, an alternative possibility, which has not yet been computationally formulated, is that these two aspects instead co-emerge interdependently from an agent's goal. Here, we describe a computational framework of goal-directed state representation in cognitive agents, in which the descriptive and prescriptive aspects of a world model co-emerge from agent-environment interaction sequences, or experiences. Drawing on Buddhist epistemology, we introduce a construct of goal-directed, or telic, states, defined as classes of goal-equivalent experience distributions. Telic states provide a parsimonious account of goal-directed learning in terms of the statistical divergence between behavioral policies and desirable experience features. We review empirical and theoretical literature supporting this novel perspective and discuss its potential to provide a unified account of behavioral, phenomenological and neural dimensions of purposeful behaviors across diverse substrates.

Learning telic-controllable state representations

Computational accounts of purposeful behavior consist of descriptive and normative aspects. The former enable agents to ascertain the current (or future) state of affairs in the world and the latter to evaluate the desirability, or lack thereof, of these states with respect to the agent's goals. In Reinforcement Learning, the normative aspect (reward and value functions) is assumed to depend on a pre-defined and fixed descriptive one (state representation). Alternatively, these two aspects may emerge interdependently: goals can be, and indeed often are, expressed in terms of state representation features, but they may also serve to shape state representations themselves. Here, we illustrate a novel theoretical framing of state representation learning in bounded agents, coupling descriptive and normative aspects via the notion of goal-directed, or telic, states. We define a new controllability property of telic state representations to characterize the tradeoff between their granularity and the policy complexity capacity required to reach all telic states. We propose an algorithm for learning controllable state representations and demonstrate it using a simple navigation task with changing goals. Our framework highlights the crucial role of deliberate ignorance - knowing what to ignore - for learning state representations that are both goal-flexible and simple. More broadly, our work provides a concrete step towards a unified theoretical view of natural and artificial learning through the lens of goals.